OpenRaiser/Intern-Atlas huggingface dataset:
Impact of Sampling on Locally Differentially Private Data Collection
Venue: arXiv cs.CR 2022
Year: 2022

With the recent bloom of data, there is a huge surge in threats against individuals' private information. Various techniques for optimizing privacy-preserving data analysis are at the focus of research in the recent years. In this paper, we analyse the impact of sampling on the utility of the standard techniques of frequency estimation, which is at the core of large-scale data analysis, of the locally deferentially private data-release under a pure protocol. We study the case in a distributed environment of data sharing where the values are reported by various nodes to the central server, e.g., cross-device Federated Learning. We show that if we introduce some random sampling of the nodes in order to reduce the cost of communication, the standard existing estimators fail to remain unbiased. We propose a new unbiased estimator in the context of sampling each node with certain probability and compute various statistical summaries of the data using it. We propose a way of sampling each node with personalized sampling probabilities as a step to further generalisation, which leads to some interesting open questions in the end. We analyse the accuracy of our proposed estimators on synthetic datasets to gather some insight on the trade-off between communication cost, privacy, and utility.
A temporal chrominance trigger for clean-label backdoor attack against anti-spoof rebroadcast detection
Venue: arXiv cs.CV 2022
Year: 2022

We propose a stealthy clean-label video backdoor attack against Deep Learning (DL)-based models aiming at detecting a particular class of spoofing attacks, namely video rebroadcast attacks. The injected backdoor does not affect spoofing detection in normal conditions, but induces a misclassification in the presence of a specific triggering signal. The proposed backdoor relies on a temporal trigger altering the average chrominance of the video sequence. The backdoor signal is designed by taking into account the peculiarities of the Human Visual System (HVS) to reduce the visibility of the trigger, thus increasing the stealthiness of the backdoor. To force the network to look at the presence of the trigger in the challenging clean-label scenario, we choose the poisoned samples used for the injection of the backdoor following a so-called Outlier Poisoning Strategy (OPS). According to OPS, the triggering signal is inserted in the training samples that the network finds more difficult to classify. The effectiveness of the proposed backdoor attack and its generality are validated experimentally on different datasets and anti-spoofing rebroadcast detection architectures.
Block-Parallel Systolic-Array Architecture for 2-D NTT-based Fragile Watermark Embedding
Venue: arXiv cs.MM 2022
Year: 2022

Number-theoretic transforms (NTTs) have been applied in the fragile watermarking of digital images. A block-parallel systolic-array architecture is proposed for watermarking based on the 2-D special Hartley NTT (HNTT). The proposed core employs two 2-D special HNTT hardware cores, each using digital arithmetic over $\mathrm{GF}(3)$, and processes $4\times4$ blocks of pixels in parallel every clock cycle. Prototypes are operational on a Xilinx Sx35-10ff668 FPGA device. The maximum estimated throughput of the FPGA circuit is 100 million $4\times4$ HNTT fragile watermarked blocks per second, when clocked at 100 MHz. Potential applications exist in high-traffic back-end servers dealing with large amounts of protected digital images requiring authentication, in remote-sensing for high-security surveillance applications, in real-time video processing of information of a sensitive nature or matters of national security, in video/photographic content management of corporate clients, in authenticating multimedia for the entertainment industry, in the authentication of electronic evidence material, and in real-time news streaming.
Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features
Venue: arXiv cs.CV 2022
Year: 2022

Recent studies show that paddings in convolutional neural networks encode absolute position information which can negatively affect the model performance for certain tasks. However, existing metrics for quantifying the strength of positional information remain unreliable and frequently lead to erroneous results. To address this issue, we propose novel metrics for measuring (and visualizing) the encoded positional information. We formally define the encoded information as PPP (Position-information Pattern from Padding) and conduct a series of experiments to study its properties as well as its formation. The proposed metrics measure the presence of positional information more reliably than the existing metrics based on PosENet and a test in F-Conv. We also demonstrate that for any extant (and proposed) padding schemes, PPP is primarily a learning artifact and is less dependent on the characteristics of the underlying padding schemes.
Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity
Venue: arXiv cs.CY 2022
Year: 2022

A central notion in U.S. copyright law is judging the substantial similarity between an original and an (allegedly) derived work. Capturing this notion has proven elusive, and the many approaches offered by case law and legal scholarship are often ill-defined, contradictory, or internally-inconsistent. This work suggests that key parts of the substantial-similarity puzzle are amendable to modeling inspired by theoretical computer science. Our proposed framework quantitatively evaluates how much "novelty" is needed to produce the derived work with access to the original work, versus reproducing it without access to the copyrighted elements of the original work. "Novelty" is captured by a computational notion of description length, in the spirit of Kolmogorov-Levin complexity, which is robust to mechanical transformations and availability of contextual information. This results in an actionable framework that could be used by courts as an aid for deciding substantial similarity. We evaluate it on several pivotal cases in copyright law and observe that the results are consistent with the rulings, and are philosophically aligned with the abstraction-filtration-comparison test of Altai.
What Are Expected Queries in End-to-End Object Detection?
Venue: arXiv cs.CV 2022
Year: 2022

End-to-end object detection is rapidly progressed after the emergence of DETR. DETRs use a set of sparse queries that replace the dense candidate boxes in most traditional detectors. In comparison, the sparse queries cannot guarantee a high recall as dense priors. However, making queries dense is not trivial in current frameworks. It not only suffers from heavy computational cost but also difficult optimization. As both sparse and dense queries are imperfect, then \emph{what are expected queries in end-to-end object detection}? This paper shows that the expected queries should be Dense Distinct Queries (DDQ). Concretely, we introduce dense priors back to the framework to generate dense queries. A duplicate query removal pre-process is applied to these queries so that they are distinguishable from each other. The dense distinct queries are then iteratively processed to obtain final sparse outputs. We show that DDQ is stronger, more robust, and converges faster. It obtains 44.5 AP on the MS COCO detection dataset with only 12 epochs. DDQ is also robust as it outperforms previous methods on both object detection and instance segmentation tasks on various datasets. DDQ blends advantages from traditional dense priors and recent end-to-end detectors. We hope it can serve as a new baseline and inspires researchers to revisit the complementarity between traditional methods and end-to-end detectors. The source code is publicly available at \url{https://github.com/jshilong/DDQ}.
Lossless Compression of Point Cloud Sequences Using Sequence Optimized CNN Models
Venue: arXiv cs.CV 2022
Year: 2022

We propose a new paradigm for encoding the geometry of point cloud sequences, where the convolutional neural network (CNN) which estimates the encoding distributions is optimized on several frames of the sequence to be compressed. We adopt lightweight CNN structures, we perform training as part of the encoding process, and the CNN parameters are transmitted as part of the bitstream. The newly proposed encoding scheme operates on the octree representation for each point cloud, encoding consecutively each octree resolution layer. At every octree resolution layer, the voxel grid is traversed section-by-section (each section being perpendicular to a selected coordinate axis) and in each section the occupancies of groups of two-by-two voxels are encoded at once, in a single arithmetic coding operation. A context for the conditional encoding distribution is defined for each two-by-two group of voxels, based on the information available about the occupancy of neighbor voxels in the current and lower resolution layers of the octree. The CNN estimates the probability distributions of occupancy patterns of all voxel groups from one section in four phases. In each new phase the contexts are updated with the occupancies encoded in the previous phase, and each phase estimates the probabilities in parallel, providing a reasonable trade-off between the parallelism of processing and the informativeness of the contexts. The CNN training time is comparable to the time spent in the remaining encoding steps, leading to competitive overall encoding times. Bitrates and encoding-decoding times compare favorably with those of recently published compression schemes.
Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code
Venue: arXiv cs.SE 2022
Year: 2022

Few-shot learning with large-scale, pre-trained language models is a powerful way to answer questions about code, e.g., how to complete a given code example, or even generate code snippets from scratch. The success of these models raises the question whether they could serve as a basis for building a wide range code generation tools. Traditionally, such tools are built manually and separately for each task. Instead, few-shot learning may allow to obtain different tools from a single pre-trained language model by simply providing a few examples or a natural language description of the expected tool behavior. This paper studies to what extent a state-of-the-art, pre-trained language model of code, Codex, may serve this purpose. We consider three code manipulation and code generation tasks targeted by a range of traditional tools: (i) code mutation; (ii) test oracle generation from natural language documentation; and (iii) test case generation. For each task, we compare few-shot learning to a manually built tool. Our results show that the model-based tools complement (code mutation), are on par (test oracle generation), or even outperform their respective traditionally built tool (test case generation), while imposing far less effort to develop them. By comparing the effectiveness of different variants of the model-based tools, we provide insights on how to design an appropriate input ("prompt") to the model and what influence the size of the model has. For example, we find that providing a small natural language description of the code generation task is an easy way to improve predictions. Overall, we conclude that few-shot language models are surprisingly effective, yet there is still more work to be done, such as exploring more diverse ways of prompting and tackling even more involved tasks.
HEX: Human-in-the-loop Explainability via Deep Reinforcement Learning
Venue: arXiv cs.LG 2022
Year: 2022

The use of machine learning (ML) models in decision-making contexts, particularly those used in high-stakes decision-making, are fraught with issue and peril since a person - not a machine - must ultimately be held accountable for the consequences of the decisions made using such systems. Machine learning explainability (MLX) promises to provide decision-makers with prediction-specific rationale, assuring them that the model-elicited predictions are made for the right reasons and are thus reliable. Few works explicitly consider this key human-in-the-loop (HITL) component, however. In this work we propose HEX, a human-in-the-loop deep reinforcement learning approach to MLX. HEX incorporates 0-distrust projection to synthesize decider specific explanation-providing policies from any arbitrary classification model. HEX is also constructed to operate in limited or reduced training data scenarios, such as those employing federated learning. Our formulation explicitly considers the decision boundary of the ML model in question, rather than the underlying training data, which is a shortcoming of many model-agnostic MLX methods. Our proposed methods thus synthesize HITL MLX policies that explicitly capture the decision boundary of the model in question for use in limited data scenarios.
On the Privacy Properties of GAN-generated Samples
Venue: arXiv cs.LG 2022
Year: 2022

The privacy implications of generative adversarial networks (GANs) are a topic of great interest, leading to several recent algorithms for training GANs with privacy guarantees. By drawing connections to the generalization properties of GANs, we prove that under some assumptions, GAN-generated samples inherently satisfy some (weak) privacy guarantees. First, we show that if a GAN is trained on m samples and used to generate n samples, the generated samples are (epsilon, delta)-differentially-private for (epsilon, delta) pairs where delta scales as O(n/m). We show that under some special conditions, this upper bound is tight. Next, we study the robustness of GAN-generated samples to membership inference attacks. We model membership inference as a hypothesis test in which the adversary must determine whether a given sample was drawn from the training dataset or from the underlying data distribution. We show that this adversary can achieve an area under the ROC curve that scales no better than O(m^{-1/4}).
Robotic Planning under Uncertainty in Spatiotemporal Environments in Expeditionary Science
Venue: arXiv cs.RO 2022
Year: 2022

In the expeditionary sciences, spatiotemporally varying environments -- hydrothermal plumes, algal blooms, lava flows, or animal migrations -- are ubiquitous. Mobile robots are uniquely well-suited to study these dynamic, mesoscale natural environments. We formalize expeditionary science as a sequential decision-making problem, modeled using the language of partially-observable Markov decision processes (POMDPs). Solving the expeditionary science POMDP under real-world constraints requires efficient probabilistic modeling and decision-making in problems with complex dynamics and observational models. Previous work in informative path planning, adaptive sampling, and experimental design have shown compelling results, largely in static environments, using data-driven models and information-based rewards. However, these methodologies do not trivially extend to expeditionary science in spatiotemporal environments: they generally do not make use of scientific knowledge such as equations of state dynamics, they focus on information gathering as opposed to scientific task execution, and they make use of decision-making approaches that scale poorly to large, continuous problems with long planning horizons and real-time operational constraints. In this work, we discuss these and other challenges related to probabilistic modeling and decision-making in expeditionary science, and present some of our preliminary work that addresses these gaps. We ground our results in a real expeditionary science deployment of an autonomous underwater vehicle (AUV) in the deep ocean for hydrothermal vent discovery and characterization. Our concluding thoughts highlight remaining work to be done, and the challenges that merit consideration by the reinforcement learning and decision-making community.
Density-Based Pruning of Drone Swarm Services
Venue: arXiv cs.RO 2022
Year: 2022

We propose a novel framework for the recommendation of swarm-based drone delivery services based on the consumers preferences. We propose a density-based pruning approach that uses the concept of partnerships with charging station providers to reduce the search space of swarm-based drone service delivery providers. A weighted service composition algorithm is proposed that considers the providers capabilities and consumers' preferences in selecting the best next service. We propose a voting-based recommendation algorithm to select the best providers. We conduct a set of experiments to evaluate the efficiency of the framework in terms of consumer satisfaction, run-time, and search space reduction cost.
12 Years of Self-tracking for Promoting Physical Activity from a User Diversity Perspective: Taking Stock and Thinking Ahead
Venue: arXiv cs.HC 2022
Year: 2022

Despite the indisputable personal and societal benefits of regular physical activity, a large portion of the population does not follow the recommended guidelines, harming their health and wellness. The World Health Organization has called upon governments, practitioners, and researchers to accelerate action to address the global prevalence of physical inactivity. To this end, an emerging wave of research in ubiquitous computing has been exploring the potential of interactive self-tracking technology in encouraging positive health behavior change. Numerous findings indicate the benefits of personalization and inclusive design regarding increasing the motivational appeal and overall effectiveness of behavior change systems, with the ultimate goal of empowering and facilitating people to achieve their goals. However, most interventions still adopt a "one-size-fits-all" approach to their design, assuming equal effectiveness for all system features in spite of individual and collective user differences. To this end, we analyze a corpus of 12 years of research in self-tracking technology for health behavior change, focusing on physical activity, to identify those design elements that have proven most effective in inciting desirable behavior across diverse population segments. We then provide actionable recommendations for designing and evaluating behavior change self-tracking technology based on age, gender, occupation, fitness, and health condition. Finally, we engage in a critical commentary on the diversity of the domain and discuss ethical concerns surrounding tailored interventions and directions for moving forward.
Offline Reinforcement Learning with Causal Structured World Models
Venue: arXiv cs.LG 2022
Year: 2022

Model-based methods have recently shown promising for offline reinforcement learning (RL), aiming to learn good policies from historical data without interacting with the environment. Previous model-based offline RL methods learn fully connected nets as world-models that map the states and actions to the next-step states. However, it is sensible that a world-model should adhere to the underlying causal effect such that it will support learning an effective policy generalizing well in unseen states. In this paper, We first provide theoretical results that causal world-models can outperform plain world-models for offline RL by incorporating the causal structure into the generalization error bound. We then propose a practical algorithm, oFfline mOdel-based reinforcement learning with CaUsal Structure (FOCUS), to illustrate the feasibility of learning and leveraging causal structure in offline RL. Experimental results on two benchmarks show that FOCUS reconstructs the underlying causal structure accurately and robustly. Consequently, it performs better than the plain model-based offline RL algorithms and other causal model-based RL algorithms.
Anomaly detection in surveillance videos using transformer based attention model
Venue: arXiv cs.CV 2022
Year: 2022

Surveillance footage can catch a wide range of realistic anomalies. This research suggests using a weakly supervised strategy to avoid annotating anomalous segments in training videos, which is time consuming. In this approach only video level labels are used to obtain frame level anomaly scores. Weakly supervised video anomaly detection (WSVAD) suffers from the wrong identification of abnormal and normal instances during the training process. Therefore it is important to extract better quality features from the available videos. WIth this motivation, the present paper uses better quality transformer-based features named Videoswin Features followed by the attention layer based on dilated convolution and self attention to capture long and short range dependencies in temporal domain. This gives us a better understanding of available videos. The proposed framework is validated on real-world dataset i.e. ShanghaiTech Campus dataset which results in competitive performance than current state-of-the-art methods. The model and the code are available at https://github.com/kapildeshpande/Anomaly-Detection-in-Surveillance-Videos
Mesh-free Eulerian Physics-Informed Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

Physics-informed Neural Networks (PINNs) have recently emerged as a principled way to include prior physical knowledge in form of partial differential equations (PDEs) into neural networks. Although PINNs are generally viewed as mesh-free, current approaches still rely on collocation points within a bounded region, even in settings with spatially sparse signals. Furthermore, if the boundaries are not known, the selection of such a region is difficult and often results in a large proportion of collocation points being selected in areas of low relevance. To resolve this severe drawback of current methods, we present a mesh-free and adaptive approach termed particle-density PINN (pdPINN), which is inspired by the microscopic viewpoint of fluid dynamics. The method is based on the Eulerian formulation and, different from classical mesh-free method, does not require the introduction of Lagrangian updates. We propose to sample directly from the distribution over the particle positions, eliminating the need to introduce boundaries while adaptively focusing on the most relevant regions. This is achieved by interpreting a non-negative physical quantity (such as the density or temperature) as an unnormalized probability distribution from which we sample with dynamic Monte Carlo methods. The proposed method leads to higher sample efficiency and improved performance of PINNs. These advantages are demonstrated on various experiments based on the continuity equations, Fokker-Planck equations, and the heat equation.
Understanding NVMe Zoned Namespace (ZNS) Flash SSD Storage Devices
Venue: arXiv cs.OS 2022
Year: 2022

The standardization of NVMe Zoned Namespaces (ZNS) in the NVMe 2.0 specification presents a unique new addition to storage devices. Unlike traditional SSDs, where the flash media management idiosyncrasies are hidden behind a flash translation layer (FTL) inside the device, ZNS devices push certain operations regarding data placement and garbage collection out from the device to the host. This allows the host to achieve more optimal data placement and predictable garbage collection overheads, along with lower device write amplification. Thus, additionally increasing flash media lifetime. As a result, ZNS devices are gaining significant attention in the research community. However, with the current software stack there are numerous ways of integrating ZNS devices into a host system. In this work, we begin to systematically analyze the integration options, report on the current software support for ZNS devices in the Linux Kernel, and provide an initial set of performance measurements. Our main findings show that larger I/O sizes are required to saturate the ZNS device bandwidth, and configuration of the I/O scheduler can provide workload dependent performance gains, requiring careful consideration of ZNS integration and configuration depending on the application workload and its access patterns. Our dataset and code are available at https: //github.com/nicktehrany/ZNS-Study.